Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H$^2$AD MIMO Receiver

TL;DR

This paper introduces a low-complexity fusion method and a multi-branch neural network to improve DOA sensing in massive H$^2$AD MIMO systems, achieving high accuracy with less prior knowledge and better performance in low-SNR conditions.

Contribution

It proposes a novel CRLB-ratio-weight fusion technique and a multi-branch DNN for enhanced DOA estimation in massive H$^2$AD MIMO, reducing complexity and prior knowledge dependence.

Findings

CRLB-ratio-WF achieves performance comparable to CRLB-based methods.

MBDNN significantly outperforms in low-SNR scenarios.

Order-of-magnitude accuracy improvement at -15 dB SNR.

Abstract

As a green MIMO structure, massive H$^2$AD is viewed as a potential technology for the future 6G wireless network. For such a structure, it is a challenging task to design a low-complexity and high-performance fusion of target direction values sensed by different sub-array groups with fewer use of prior knowledge. To address this issue, a lightweight Cramer-Rao lower bound (CRLB)-ratio-weight fusion (WF) method is proposed, which approximates inverse CRLB of each subarray using antenna number reciprocals to eliminate real-time CRLB computation. This reduces complexity and prior knowledge dependence while preserving fusion performance. Moreover, a multi-branch deep neural network (MBDNN) is constructed to further enhance direction-of-arrival (DOA) sensing by leveraging candidate angles from multiple subarrays. The subarray-specific branch networks are integrated with a shared regression module to effectively eliminate pseudo-solutions and fuse true angles. Simulation results show that the proposed CRLB-ratio-WF method achieves DOA sensing performance comparable to CRLB-based methods, while significantly reducing the reliance on prior knowledge. More notably, the proposed MBDNN has superior performance in low-SNR ranges. At SNR $= -15$ dB, it achieves an order-of-magnitude improvement in estimation accuracy compared to CRLB-ratio-WF method.